Effects of incomplete remediation of NAPL-contaminated aquifers: experimental and numerical modeling investigations
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Abstract
The benefits of partial source zone treatment of non-aqueous phase liquids (NAPL)-contaminated sites (not fully removing the entrapped free-phase NAPL sources) with respect to achieving cleanup goals and reducing concentrations of dissolved constituents in downstream plumes are being debated. Uncertainty associated with the removal of NAPLs from source zones could be attributed to a number of factors including lack of information on the extent or timing of spills, complex entrapment configurations created by unstable behavior (fingering), geologic heterogeneity, and unavailability of accurate techniques for characterizing these heterogeneities, and uncertainty in locating source zone and estimating NAPL mass. Data for the resolution of issues related to benefits of partial source zone treatment are not expected to come from field sites. Laboratory studies in intermediate-scale test tanks can provide accurate data sets to investigate this issue, as it is possible to conduct controlled experiments under known conditions of aquifer heterogeneity. At this scale, source depletion and downstream concentrations in dissolved plumes can be monitored during remediation. The data generated in controlled experiments are used to validate numerical models to conduct theoretical analysis. This paper discusses this approach and presents results from such a study where the benefits of partial source zone treatment using surfactants were evaluated using intermediate-scale testing and numerical modeling. Results from both experiment and numerical simulations agreed conceptually where they suggested that a very large fraction of NAPL has to be removed from the entrapment zone to significantly reduce downstream plume concentrations.
Keywords
Dissolution DNAPL Heterogeneity Numerical modeling SurfactantsAbbreviations
- b
Sorption parameter in the Langmuir isotherm (L^{3} M^{−1})
- c
Aqueous concentration (M L^{−3})
- c*
Apparent aqueous solubility of PCE in the presence of Tween-80 (M L^{−3})
- c_{s}
Aqueous solubility of PCE under normal condition (i.e., no surfactant) (M L^{−3})
- c_{tw}
Aqueous concentration of Tween-80 (M L^{−3})
- d_{50}
Average grain size (L)
- D_{m}
Molecular diffusion coefficient (L^{2} T^{−1})
- J
NAPL-water mass transfer rate per unit volume of porous medium (M L^{−3} T^{−1})
- k_{La}
Overall mass transfer coefficient (T^{−1})
- k_{r,w}
Relative permeability of water in the sand (−)
- K
Effective hydraulic conductivity (L T^{−1})
- K_{s}
Saturated hydraulic conductivity (L T^{−1})
- \( \overline{{\ln \,K_{\text{s}} }} \)
Average of (natural) log of K_{s}
- L*
Characteristic or dissolution length (L)
- m_{0}
Original PCE mass in the source zone
- Pe
Peclet number (−)
- Re
Reynolds number (−)
- s
Fraction of sorbed mass (M M^{−1})
- \( s_{{\ln K_{\text{s}} }}^{2} \)
Variance of (natural) log of K_{s}
- S_{n}
PCE saturation (−)
- \( \bar{S}_{n} \)
Average PCE saturation (−)
- S_{r,w}
Residual water saturation (−)
- S_{w}
Water saturation (−)
- Sc
Schmidt number (−)
- Sh
Modified Sherwood number (−)
- Sh_{N}
Modified Sherwood number for natural dissolution (−)
- Sh_{E}
Modified Sherwood number for surfactant-enhanced dissolution (−)
- \( \Updelta m_{i} \)
The amount of PCE mass removed from the source zone after application of surfactant injection of time \( \Updelta t_{i} \) (M)
- \( \Upgamma_{\hbox{max} } \)
Sorption parameter in Langmuir isotherm (M M^{−1})
- \( \theta_{n} \)
Volumetric NAPL content (−)
- τ
Tortuosity factor (−)
Introduction
Non-aqueous phase liquids (NAPLs) such as gasoline and chlorinated solvents are common organic pollutants found in contaminated soils and aquifers (Mercer and Cohen 1990). These organic liquids often are entrapped in the subsurface where they slowly dissolve into flowing groundwater and generate downstream contaminant plumes in which concentrations exceed regulatory standards. Although several technologies have been proposed recently for NAPL cleanup, the ability to completely remediate these contaminated sites is still in doubt. Such uncertainty is not only caused by the ineffectiveness of the remediation technique, but could also be attributed to the unavailability of methods for characterizing aquifer heterogeneities and the inability to accurately characterize the saturation distribution of NAPL in the source zone. These factors result in errors in both locating and estimating volume of NAPL in the source zone. Consequently, complete removal of NAPL mass from the source zone may become impossible or impractical using any available cleanup technology.
Geologic heterogeneity is one of the primary factors that controls migration and distribution of NAPL in the subsurface (Kueper et al. 1989; Kueper and Frind 1991; Kueper and Gerhard 1995; Illangasekare et al. 1995; Illangasekare 1998; Dekker and Abriola 2000; Bradford et al. 2000). It also complicates characterization of the source zone (Nelson et al. 1999) and effectiveness of NAPL remediation (Ewing 1996; Meinardus et al. 2002; Saenton et al. 2002). Saenton et al. (2002) demonstrated that uncharacterized aquifer heterogeneity can cause significant uncertainty in the removal of NAPL from source zones. In that study, the effectiveness of surfactant-enhanced aquifer remediation (SEAR) for removal of p-xylene-contaminated soils in heterogeneous formations was investigated. A numerical model, validated using data generated in test tanks, was used to simulate mass removal during surfactant injection. Analysis of numerical model results showed that the required cleanup time depends on how NAPL is distributed in the source zone after a spill. The distribution of entrapped NAPL is referred to in this paper as entrapment architecture. In some cases, some of the entrapped NAPL was not removed even after long treatment periods because the treating reagents (e.g., surfactants) completely bypassed low permeability zones. Conventional pump-and-treat remediation methods have been found to be ineffective in removing significant NAPL mass due to low rates of natural dissolution. Remediation engineers have now focused on more aggressive NAPL removal technologies such as enhanced bioremediation, in situ chemical oxidation, co-solvent flushing, and SEAR which is of interest in this work.
Sale and McWhorter (2001) point out the limited near-term benefits of partial source zone treatment in achieving cleanup goals with expected reduction of dissolved contaminant concentrations downstream of the source zone. They conclude that mass removal from source zones is ineffective unless a large fraction of the entrapped free-phase NAPL is removed.
Sale and McWhorter (2001) study however considered only very simplified source architecture and flow conditions. These simplifications included uniform flow fields, simple entrapment architecture that consisted of only NAPL pools and a dissolution model that did not allow for changes in NAPL saturation. Hence, it may be premature to generalize the applicability of their findings to field sites that produce complex NAPL entrapment configurations and non-uniform flow fields under heterogeneous conditions.
This paper further investigates this issue by considering the application of SEAR technology that may only partially remove free-phase NAPL from a heterogeneous source zone. Analysis is conducted at intermediate scales using two-dimensional (2-D) test tanks to create heterogeneous systems with complex entrapment architecture and non-uniform flow fields. Concentrations in the downstream dissolved plume, which are a function of mass depletion rate or the amount of NAPL mass left in the source zone, are monitored and the consequences of incomplete source zone treatment are evaluated.
Background
Dissolution of non-aqueous phase liquids
Although 1-D column experiments provide a fundamental understanding of the processes or parameters that govern mass transfer, it is necessary to have a dissolution model that can account for the effect of flow bypassing (Saba and Illangasekare 2000) as well as scale dependency on mass transfer (Saba 1999; Nambi and Powers 2000; Schaerlaekens and Feyen 2004). Saba and Illangasekare (2000) demonstrated that neglecting the dimensionality of the flow field can result in orders of magnitude errors in estimates of mass transfer coefficients using these empirical relationships. Therefore, in this study, the dissolution model is calibrated with a 2-D experiment and is used in subsequent numerical modeling investigation.
Enhanced dissolution using surfactants
A number of laboratory studies have demonstrated that surfactants can effectively remove entrapped DNAPLs from contaminated soils (Fountain et al. 1991; Pennell et al. 1993; McCray and Brusseau 1998; McCray et al. 2001). Surfactant-enhanced NAPL recovery has also been implemented at field sites, which in some cases have produced promising results (Abdul et al. 1992; Martel et al. 1998).
Equations (2) and (3) are used to describe natural and enhanced dissolution of entrapped DNAPL.
DNAPL mass removal in the upscaled domain
The removal of entrapped DNAPL either from natural or enhanced dissolution in the upscaled domain has been investigated through numerical modeling study by several researchers in recent years (Parker and Park 2004; Park and Parker 2005; Grant et al. 2007; Saenton and Illangasekare 2007; Maji and Sudicky 2008; Basu et al. 2008). Among these studies, Parker and Park (2004) and Park and Parker (2005) proposed and verified an upscaled dissolution model where the temporal downstream concentration as well as mass flux from the entrapped DNAPL were a function of average groundwater velocity and, more importantly, a function of the ratio between DNAPL mass at any time to the original spilled mass to some power or (M/M_{0})^{ β }. Saenton and Illangasekare (2007), on the other hand, developed an upscaled natural dissolution model which considered soil’s heterogeneity (in terms of variance of ln K), spatial distribution and spreading of DNAPL mass in the source zone (in terms of spatial moment), and a dissolution length (Saba and Illangasekare 2000). These methods, of course, require intensive amount of data acquisition which sometimes cannot be obtained from the field investigation.
Several simple, but very powerful, relationships for predicting mass flux (and/or concentration) based on the amount DNAPL mass left in the source zone have been proposed by several studies (Falta et al. 2005; Brusseau et al. 2008; DiFilippo and Brusseau 2011). These relationships enable the first estimation of mass flux (or concentration) without having to rely on massive site data except for the geometry or the distribution of DNAPL in the entrapment zone (e.g., GTP or ganglia-to-pool ratio).
Numerical models
NAPL migration model
The development of many multiphase flow models has been reported (Kaluarachchi and Parker 1990; Sleep and Sykes 1993; Delshad et al. 1996; Dekker and Abriola 2000). In general, these models are designed to solve a coupled set of partial differential equations representing two-phase flow in porous media. An example model is UTCHEM (Delshad et al. 1996), which is a 3-D, multi-component, compositional finite-difference simulator for multiphase flow and contaminant transport in porous media.
Natural and enhanced dissolution model
Experiments
Natural and surfactant-enhanced dissolution experiments were conducted in a bench-scale, 2-D horizontal dissolution cell containing a NAPL source in the middle of the tank. The purpose of these experiments was to obtain natural and surfactant-enhanced dissolution datasets necessary for model calibration and validation. The validated numerical models were then used to conduct theoretical analyses on the dissolution of NAPL in heterogeneous aquifers.
Materials
Properties of tetrachloroethene or PCE at 25 °C
Properties | Value | Unit |
---|---|---|
Chemical formula | C_{2}Cl_{4} | – |
Molecular weight^{a} | 165.8 | g mol^{−1} |
Density^{a} | 1.62 | g cm^{−3} |
Aqueous solubility^{b} | 200 | mg L^{−1} |
Diffusion coefficient^{b} | 8.19 × 10^{−10} | m^{2} s^{−1} |
Viscosity | <1 | cp |
MCL | 0.005 | mg L^{−1} |
Properties of Tween-80 (surfactant)
Properties | Value | Unit |
---|---|---|
Average molecular weight | 1310.0^{a} | g mol^{−1} |
Density (5 %) | 1.064^{b} | g cm^{−3} |
Dynamic viscosity (5 %) | 1.117^{b} | cp |
CMC | 13.0^{a} | mg L^{−1} |
Properties of sands used in the experiments and numerical simulation studies
Sand sieve sizes | K_{s} (×10^{−3} m s^{−1})^{a} | d_{50} (×10^{−3} m) (Unimin Corp.) | S_{r,w} (−)^{a} | PCE sorption, K_{d} (L kg^{−1}) | Surfactant sorption (Langmuir isotherm)^{c} | |
---|---|---|---|---|---|---|
Γ_{max} (kg kg^{−1}) | b (L kg^{−1}) | |||||
#16 | 8.03 | 1.08 | 0.07 | 1.53^{b} | 9.1 × 10^{−3b} | 42.6^{b} |
#30 | 1.98 | 0.50 | 0.26 | 1.04^{d} | 6.0 × 10^{−3d} | 1.04^{d} |
#50 | 0.406 | 0.32 | 0.29 | 1.27^{d} | – | – |
#70 | 0.243 | 0.20 | 0.30 | 1.52^{b} | 8.7 × 10^{−3b} | 42.5^{b} |
#110 | 0.053 | 0.12 | 0.26 | 1.33^{d} | – | – |
Dissolution experiments
2-D horizontal dissolution cell
For this experiment, the source size was 10.5 cm long and 5.0 cm wide and 5.0 cm high (Fig. 2). The initially water-wet source zone was filled to approximately one-third of total source thickness with red-dyed (Sudan IV, Sigma Chemicals) tetrachloroethene or PCE (Aldrich Chemicals). A volume of 31.0 cm^{3} PCE was injected into the source zone using a glass syringe under no flow conditions. PCE was injected into the source zone through 15 equally spaced injection ports located on the top of the test tank. The tank was then flipped over to allow the injected PCE to be entrapped in the source zone. After 2 days, the tank was flipped back allowing PCE to sink due to gravity for 24 h. In this way, a fully developed saturation profile was generated where NAPL saturation ranged from residual in the upper part of the source zone to high saturation at the bottom of the source zone.
Constant head reservoirs were installed upstream and downstream of the test cell to maintain a continuous and steady flow of deionized water (ROPure^{®}, Barnstead-Thermolyne Corporation) through the test cell. An array of five injection ports located upstream of the source zone was installed for use in enhanced dissolution experiments. A multi-channel syringe pump (Soil Measurement Systems) was used to provide a constant injection rate for the surfactant solution. Nine sampling ports located downstream were used to extract aqueous solution samples. The effluent port was used as an additional sampling port to monitor both total flow rate and mass balance. It should be noted that these experiments were conducted without replicate due to the difficulty in reproducing identical NAPL saturation profile.
Natural dissolution
Enhanced dissolution
Numerical model calibration
Natural dissolution
Both steady-state and transient dissolution data from dissolution experiments conducted at various effluent flow rates were used in the model calibration. A large amount of data were used as observations in regression analysis in an effort to assure that the calibrated dissolution model with optimized parameters would be able to simulate transient and steady-state natural dissolution over a wide range of hydrodynamic conditions. The calibrated parameters were the empirical coefficients (α_{1}, α_{2},…,α_{4}) that appear in Eq. (2). Normal dissolution of PCE was simulated using the dissolution module that was described earlier in Sect. 3.
Model parameters used in natural and surfactant-enhanced dissolution simulations
Parameters | Value | Unit |
---|---|---|
Δx = Δy = Δz | 1.0 | cm |
Number of columns | 54 | – |
Number of rows | 45 | – |
Number of layers | 5 | – |
K_{s} (#70) | 0.81^{a} | cm min^{−1} |
K_{s} (#16) | 25.8^{a} | cm min^{−1} |
Porosity (#70) | 0.45^{a} | – |
Porosity (#16) | 0.48^{a} | – |
Longitudinal dispersivity | 0.035^{a,b} | cm |
Transverse dispersivity | 0.028^{a,b} | cm |
Optimized empirical parameters with their 95 % confidence interval and composite-scaled sensitivities for normal dissolution (Eq. 2)
Parameters | Value ± 95 % CI | Sensitivity |
---|---|---|
α _{1} | 12.41 ± 0.71 | 105.7 |
α _{2} | 0.23 ± 0.02 | 159.0 |
α _{3} | 0.5 | – |
α _{4} | 1.28 ± 0.28 | 127.4 |
The sensitivity of the Reynolds number exponent, α_{2}, was highest among the three parameters and this reflected the strong dependence of the mass transfer rate on hydrodynamic conditions. The last term in Eq. (2) or (θ_{ n }d_{50}/τL*) that represents pore geometry in the finite-difference grid block had a relatively high sensitivity for its exponent, α_{4}, and this reflected the relative importance of the NAPL morphology in mass transfer. The UCODE-generated correlation coefficient (r) matrix indicated that there was no significant correlation among the three parameters (i.e., all r’s < 0.75) and this implied the independence of the parameters from one another. Figures 3 and 4 illustrate examples of the experimental data and the model fit for steady-state and transient dissolution under natural conditions. As can be seen from these figures, the natural dissolution model was able to capture the mass transfer characteristic well.
Enhanced dissolution
Similar to natural dissolution, the surfactant-enhanced dissolution option in the dissolution module must also be calibrated. The simulation of enhanced dissolution, however, required mass transfer coefficients for both normal and enhanced conditions. This was because during surfactant injection, the surfactant solution bypassed some numerical grid blocks due to a permeability contrast. In these grid blocks, dissolution occurred under natural conditions. Optimized parameters obtained from Sect. 4.3.1 were used for calculating mass transfer coefficients under natural conditions. Therefore, the calibrated parameters are β_{1}, β_{2}, …, β_{5}, the same that appear in Eq. (3). The transient breakthrough curves shown in Fig. 5 were used for model calibration.
The numerical model setup used the same discretized finite-difference grid system as in the natural dissolution simulation (Table 4). Once the forward simulation executed successfully, the UCODE program (Phase 2) was used to calculate parameter sensitivities. Results were similar to natural dissolution simulation. Sensitivity for the Schmidt number exponent β_{3} was extremely small compared to other parameters, and it is therefore set as β_{3} = 0.5.
It is interesting that the sensitivity for β_{4}, the exponent for θ_{ n }/(1 − θ_{ n }) term, was highest among the four calibrated parameters. This could be explained by a rapid change in volumetric NAPL content during enhanced dissolution in the presence of surfactant as PCE solubility increased significantly. This analysis confirms the finding by Saba et al. (2001). The rapid change in volumetric NAPL content (or NAPL saturation) also modified the groundwater flow field and, as a result, the Reynolds number exponent became the second largest sensitive parameter.
Optimized empirical parameters with their 95 % confidence interval and composite-scaled sensitivities for enhanced dissolution (Eq. 3)
Parameters | Value ± 95 % CI | Sensitivity |
---|---|---|
β _{1} | 0.53 ± 0.11 | 132.2 |
β _{2} | 1.87 ± 0.46 | 207.1 |
β _{3} | 0.5 | – |
β _{4} | 0.19 ± 0.04 | 319.6 |
β _{5} | 0.20 ± 0.09 | 41.2 |
As shown in Fig. 5, the calibrated enhanced dissolution model simulated the surfactant-flushing breakthrough curves relatively well. No significant correlation was found among parameters except β_{2} and β_{4}, where the correlation coefficient was r = 0.74. This could be explained by the fact that the groundwater flow velocity described in terms of the Reynolds number is closely related to the volumetric NAPL content θ_{ n } in the grid block. Figure 6 shows the model’s capability to simulate the effect of partial source zone treatment where PCE is partially removed from the source zone. The dashed line represents the model prediction that implies almost complete removal (>99 %) of entrapped DNAPL was required in order to lower the downstream concentration to the MCL.
Based on the numerical modeling presented in this section, we obtained a calibrated mass transfer model that can be used to predict mass transfer from this particular source zone. This dissolution model is able to simulate the dissolution of entrapped PCE ranging from residual to pool saturation for 10^{−4} ≤ Re ≤ 10^{−2}: a typical natural groundwater flow condition. It is, however, expected that the constitutive relationships as shown in Eqs. (2) and (3) can be extrapolated to conditions outside the reported range.
Numerical simulations study of partial source zone treatment
The goal of this numerical simulation experiment was to evaluate the effect on the downstream plume concentration of partial removal of NAPL mass from the source zone that was observed in the experiments. The approach was to conduct Monte Carlo-based numerical experiments using 80 hypothetical heterogeneous aquifers containing PCE distributions created from spill simulations with UTCHEM. These hypothetical aquifers were similar to those used in the study by Saenton and Illangasekare (2007).
Intermediate-scale test aquifer
The heterogeneity was designed as a spatially correlated random field with statistical parameters similar to heterogeneous field sites. This heterogeneous packing assumed a log-normal distribution of hydraulic conductivity with \( \overline{{\ln K_{\text{s}} }} \) of 4.18 (K_{s} is in cm h^{−1}) and a variance \( s_{{\ln K_{\text{s}} }}^{2} \) of 1.22. The correlation lengths in lateral (λ_{h}) and vertical (λ_{v}) directions were 50.8 and 5.08 cm, respectively. The heterogeneous zone consisted of 1,280 cells of 25.4 cm in length and 2.54 cm in depth. The 32 columns and 40 layers resulted in 16 lateral and 20 vertical correlation lengths. A more detailed discussion of tank setup and packing can be found in Barth (1999) and Barth et al. (2001a, b).
DNAPL source zone
The NAPL source zone (1.6 × 1.0 × 0.05 m^{3}) was created from a spill simulation using UTCHEM while all subsequent mass transfer simulations were conducted using MODFLOW/RT3D described in Sect. 3. The location of the source zone is shown in Fig. 7. The source zone was discretized into 1 row, 40 layers and 24 columns. The Brooks–Corey model (1966) was used to represent the capillary pressure–saturation relationship. Total PCE spill volume was 1.0 L, the average saturation was \( \bar{S}_{n} = 0.036 \), and the spill rate was 1.67 L day^{−1}. The spill location was placed at the center of the top layer. The spill simulation was executed until the spill, or migration of PCE reached a static state. Note that in all simulations, the finest sand (#110) was replaced with clay in order to generate a realistic impermeable barrier that could be encountered during DNAPL migration in the field soils.
Surfactant-enhanced remediation simulation
This section investigates the effect of partial source zone treatment on the reduction of the contaminant concentration level at a compliance plane or monitoring well located downstream. Instead of arbitrarily removing the mass from the source zone, the surfactant technology was selected because it effectively and rapidly removes free-phase NAPL. More importantly, the effect of flow bypassing due to a hydrodynamic constraint can be included in the analysis.
In surfactant-enhanced remediation simulation, a fully penetrated extraction well was placed at the downstream end of the source zone (i.e., same location as monitoring well #1). A surfactant solution containing Tween-80 at a concentration of 50.0 g L^{−1} was continuously injected upstream of the source zone through a fully penetrated injection well at the flow rate of 1.60 cm^{3} min^{−1}. This flow rate is approximately 60 % of the total steady-state groundwater flow rate of the tank under a constant hydraulic gradient of 10^{−3}.
There are a few interesting cases where the mass reduction rate was nearly zero as indicated in Fig. 9 by nearly horizontal % mass lines. After significant removal of the easily accessible entrapped PCE mass, high saturation entrapped PCE in the pool was completely bypassed by the flowing groundwater. Despite a relatively large PCE mass fraction remaining in the source, natural dissolution resulted in an insignificant amount of dissolved mass flux or concentration.
To further investigate the benefits of partial source zone treatment, stepwise injection of the surfactant solution was conducted. The procedure was similar to what was used in the experiment. After injecting surfactant solution of duration Δt_{ i }, PCE mass of Δm_{ i } was removed from the source zone. Then, the aquifer was relaxed (i.e., no surfactant injection) and the natural dissolution model was used to simulate the steady-state PCE concentration in the monitoring well c_{ i } under the normal hydraulic gradient of 10^{−3}. This steady-state PCE concentration c_{ i } was recorded as a function of percent mass removal or (m_{0} − Δm_{ i })/m_{0}, where m_{0} is the original mass of PCE in the source zone. These alternate processes were repeated until the mass removal was close to completion or (m_{0} − Δm_{ i })/m_{0} → 0.
Conclusions
This study investigated the effects of partial removal of NAPLs from source zones. A previous investigation by Sale and McWhorter (2001) pointed out the limited near-term benefits of partial source zone treatment in achieving cleanup goals. The conclusions of their study could not be generalized because of simplifying assumptions that were made with respect to the source architecture and flow field. Our study presented a methodology that used experimentally validated numerical models that simulate natural and enhanced dissolution in heterogeneous aquifers where NAPLs are entrapped in complex architecture. Results based on 80 realizations suggest that a very large fraction of NAPL mass has to be removed from the source zone to make any significant impact on the plume concentrations downstream of the source zone. However, this study and Saenton et al. (2002) demonstrate that source zone cleanup technologies which involve the delivery of treating chemicals (e.g., surfactants) may not be as effective as expected because the ability to fully deliver these reagents is hindered by the bypassing effect.
Notes
Acknowledgments
The authors gratefully acknowledge the following funding sources: (1) National Science Foundation Major Research Instrumentation Award BES-997708, (2) National Science Foundation Award EAR-0107095, (3) Department of Defense SERDP Grant CU-1294, and (4) Faculty of Science, Chiang Mai University. Special thanks to Dr. Gilbert R. Barth who provided the spatially correlated hydraulic conductivity fields.
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